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app.py

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+#!/usr/bin/env python3
+"""
+Universal Multi-Agent Platform - Core Application (Production Ready)
+Auto-generated with Gradio 4.x compatibility
+"""
+
+import gradio as gr
+import pandas as pd
+from typing import Dict, Any, List, Optional, Tuple
+from pathlib import Path
+import json
+import os
+
+# ============================================================================
+# IMPORT ENABLED PLUGINS
+# ============================================================================
+from plugins.processors.schema_detector import *
+from plugins.processors.text_processor import *
+from plugins.outputs.table_formatter import *
+from plugins.processors.date_normalizer import *
+from plugins.file_handlers.csv_handler import *
+from plugins.outputs.report_generator import *
+from plugins.file_handlers.excel_handler import *
+from plugins.memory.document_memory import *
+from plugins.processors.data_cleaner import *
+from plugins.analyzers.statistical_analyzer import *
+from plugins.analyzers.time_series_analyzer import *
+from plugins.outputs.chart_generator import *
+from plugins.memory.conversation_memory import *
+
+# ============================================================================
+# PLUGIN MANAGER (Handles all plugin interactions)
+# ============================================================================
+
+class PluginManager:
+    """Manage all plugins and application state."""
+    
+    def __init__(self):
+        # Initialize file handlers
+        self.file_handlers = []
+        self.file_handlers.append(CSVHandler())
+        self.file_handlers.append(ExcelHandler())
+        
+        # Initialize processors/analyzers
+        self.data_cleaner = DataCleaner() if True else None
+        self.time_series_analyzer = TimeSeriesAnalyzer() if True else None
+        self.statistical_analyzer = StatisticalAnalyzer() if True else None
+
+        # Initialize memory/outputs
+        self.conversation_memory = ConversationMemory() if True else None
+        self.table_formatter = TableFormatter() if True else None
+        self.chart_generator = ChartGenerator() if True else None
+        
+        # Data storage
+        self.loaded_data: Optional[Dict[str, Any]] = None
+        self.cleaned_df: Optional[pd.DataFrame] = None
+        self.last_chart_json: Optional[str] = None
+    
+    def load_file(self, file_path: str) -> Dict[str, Any]:
+        """Load file using appropriate handler and automatically clean data."""
+        self.loaded_data = None
+        self.cleaned_df = None
+        self.last_chart_json = None
+        
+        if not os.path.exists(file_path):
+            return {"success": False, "error": "File not found on server"}
+
+        for handler in self.file_handlers:
+            if handler.can_handle(file_path):
+                result = handler.load(file_path)
+                if result.get("success"):
+                    self.loaded_data = result
+                    
+                    # Auto-clean tabular data
+                    df = self._get_raw_df()
+                    if df is not None and self.data_cleaner:
+                        df = self.data_cleaner.clean_dataframe(df)
+                        self.cleaned_df = self.data_cleaner.enforce_schema(df)
+                        
+                        if "metadata" not in result:
+                            result["metadata"] = {}
+                        result["metadata"]["cleaned_shape"] = list(self.cleaned_df.shape)
+                        result["metadata"]["cleaned_cols"] = list(self.cleaned_df.columns)
+                    
+                    return result
+        
+        return {"success": False, "error": "No handler found for this file type"}
+    
+    def _get_raw_df(self) -> Optional[pd.DataFrame]:
+        """Internal method to extract a DataFrame from loaded_data."""
+        if not self.loaded_data:
+            return None
+        if "combined" in self.loaded_data and isinstance(self.loaded_data["combined"], pd.DataFrame):
+            return self.loaded_data["combined"]
+        elif "data" in self.loaded_data and isinstance(self.loaded_data["data"], pd.DataFrame):
+            return self.loaded_data["data"]
+        return None
+
+# Initialize plugin manager
+pm = PluginManager()
+
+# ============================================================================
+# GRADIO INTERFACE LOGIC
+# ============================================================================
+
+def upload_file(file):
+    """Handle file upload."""
+    if file is None:
+        return "❌ No file uploaded", None
+    
+    try:
+        result = pm.load_file(file.name)
+        
+        if result.get("success"):
+            # Get appropriate handler for preview
+            preview_html = "Data loaded successfully"
+            for handler in pm.file_handlers:
+                if handler.can_handle(file.name) and hasattr(handler, 'preview'):
+                    preview_html = handler.preview(result)
+                    break
+            
+            shape_info = f"Shape: {pm.cleaned_df.shape}" if pm.cleaned_df is not None else "Non-tabular data"
+            
+            summary = "✅ File loaded and processed successfully\n"
+            summary += f"Type: {result.get('file_type', 'unknown')}\n"
+            summary += f"Data: {shape_info}\n\n"
+            summary += "Ready for conversational analysis!"
+            
+            return summary, preview_html
+        
+        return f"❌ Error: {result.get('error')}", None
+    
+    except Exception as e:
+        return f"❌ Critical Error: {str(e)}", None
+
+
+def process_query(query: str, history: List) -> Tuple[List, str, Optional[str]]:
+    """
+    Executes conversational analytics.
+    Returns: updated history, empty query text, and chart JSON.
+    """
+    
+    if not query or not query.strip():
+        return history + [("", "❌ Please enter a question")], "", None
+    
+    if pm.conversation_memory:
+        pm.conversation_memory.add_message("user", query)
+    
+    df = pm.cleaned_df
+    pm.last_chart_json = None
+    
+    # Handle No Data Case
+    if df is None or df.empty:
+        # Check if non-tabular data was loaded
+        if pm.loaded_data and pm.loaded_data.get('file_type') in ['pdf', 'docx']:
+            document_text = pm.loaded_data.get('text', '') or str(pm.loaded_data.get('text_data', [{}])[0].get('text', 'No text'))
+            response = "📄 **Document Content Loaded**\n\n"
+            response += "The system has loaded a document. Advanced NLP analysis would be applied here.\n"
+            response += f"Text Sample: {document_text[:200]}..."
+        else:
+            response = "❌ No **data** loaded for analysis. Please upload a file first."
+            
+        if pm.conversation_memory: 
+            pm.conversation_memory.add_message("assistant", response)
+        return history + [(query, response)], "", None
+    
+    try:
+        # Execute Analytics
+        if pm.time_series_analyzer:
+            description, result_df = pm.time_series_analyzer.analyze_query(df, query)
+        elif pm.statistical_analyzer:
+            stats = pm.statistical_analyzer.analyze(df)
+            description = "📊 Statistical Analysis Results"
+            result_df = pd.DataFrame(stats.get('columns', {})).T
+        else:
+            description = "⚠️ No analyzer available. Upload data and try basic queries."
+            result_df = None
+
+        final_response = f"**Query:** {query}\n\n{description}\n\n"
+        chart_json = None
+        
+        if result_df is not None and not result_df.empty:
+            # Format Table Output
+            if pm.table_formatter:
+                table_markdown = pm.table_formatter.format_to_markdown(result_df.head(10))
+                final_response += "### Results (Top 10 Rows):\n"
+                final_response += table_markdown
+                final_response += f"\n\n*Total Rows: {len(result_df):,}*"
+            
+            # Generate Chart Output
+            if pm.chart_generator and len(result_df.columns) >= 2:
+                try:
+                    x_col = result_df.columns[0]
+                    y_col = result_df.columns[1]
+                    chart_json = pm.chart_generator.create_chart_html(
+                        result_df.head(20), 
+                        'bar', 
+                        x=x_col, 
+                        y=y_col,
+                        title=description.split('\n')[0][:50]
+                    )
+                except Exception as chart_err:
+                    print(f"Chart generation failed: {chart_err}")
+
+        else:
+            final_response = f"**Query:** {query}\n\n{description}"
+        
+        if pm.conversation_memory: 
+            pm.conversation_memory.add_message("assistant", final_response)
+        
+        return history + [(query, final_response)], "", chart_json
+    
+    except Exception as e:
+        import traceback
+        error_trace = traceback.format_exc()
+        response = f"❌ Analysis Error: {str(e)}\n\nDebug Info:\n```\n{error_trace[:500]}\n```"
+        return history + [(query, response)], "", None
+
+
+def create_ui():
+    """Create Gradio interface (Gradio 4.x compatible)."""
+    
+    with gr.Blocks(title="Universal AI Platform", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🤖 Universal Multi-Agent Platform")
+        gr.Markdown("## AI-Powered Analysis & Conversational Intelligence")
+        
+        with gr.Tabs():
+            # ================================================================
+            # FILE UPLOAD TAB
+            # ================================================================
+            with gr.Tab("📁 Upload & Process"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        file_upload = gr.File(
+                            label="Upload Your File",
+                            file_types=[".xlsx", ".xls", ".csv", ".pdf", ".docx", ".json", ".xml"],
+                            interactive=True
+                        )
+                        upload_btn = gr.Button("📤 Process File", variant="primary", size="lg")
+                        upload_status = gr.Textbox(
+                            label="Status", 
+                            lines=8, 
+                            value="Ready to process files. Supported: Excel, CSV, PDF, Word, JSON, XML", 
+                            interactive=False
+                        )
+                    
+                    with gr.Column(scale=2):
+                        data_preview = gr.HTML(label="Data Preview")
+                
+                upload_btn.click(
+                    fn=upload_file,
+                    inputs=[file_upload],
+                    outputs=[upload_status, data_preview]
+                )
+            
+            # ================================================================
+            # CHAT INTERFACE TAB
+            # ================================================================
+            with gr.Tab("💬 Ask Questions"):
+                chatbot = gr.Chatbot(
+                    height=450,
+                    label="Conversational AI Assistant",
+                    type='tuples',
+                    show_copy_button=True
+                )
+                
+                gr.Markdown("""
+                ### 📝 Example Queries:
+                - "Summarize the data"
+                - "Show me aggregated statistics"
+                - "Group by [column name]"
+                - "Segment the data into categories"
+                - "Analyze trends over time"
+                - "Show correlation between columns"
+                """)
+                
+                with gr.Row():
+                    msg = gr.Textbox(
+                        label="Your Query",
+                        placeholder="Ask anything about your data...",
+                        scale=4,
+                        lines=2
+                    )
+                    submit_btn = gr.Button("Send", variant="primary", scale=1, size="lg")
+                
+                # Chart display area
+                chart_display = gr.HTML(
+                    label="Visualization",
+                    value=""
+                )
+                
+                # Clear button
+                with gr.Row():
+                    clear_btn = gr.Button("🗑️ Clear Chat", variant="secondary")
+                
+                def process_and_display(query: str, history: List) -> Tuple[List, str, str]:
+                    """Process query and return chart HTML."""
+                    updated_history, empty_msg, chart_json_str = process_query(query, history)
+                    
+                    # Convert chart JSON to HTML with embedded Plotly
+                    # KEY FIX: Use string concatenation instead of f-string substitution
+                    chart_html = ""
+                    if chart_json_str:
+                        # Build the HTML string using concatenation to avoid f-string issues
+                        chart_html = (
+                            '<div style="width: 100%; height: 500px; margin-top: 20px;">' +
+                            '<script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>' +
+                            '<div id="plotly-chart-container"></div>' +
+                            '<script>' +
+                            '(function() {' +
+                            'try {' +
+                            'const chartData = ' + chart_json_str + ';' +
+                            "Plotly.newPlot('plotly-chart-container', chartData.data, chartData.layout, {responsive: true, displayModeBar: true});" +
+                            '} catch (e) {' +
+                            "console.error('Chart rendering error:', e);" +
+                            "document.getElementById('plotly-chart-container').innerHTML = '<p style=\"color: red; padding: 20px;\">Chart rendering failed: ' + e.message + '</p>';" +
+                            '}' +
+                            '})();' +
+                            '</script>' +
+                            '</div>'
+                        )
+                    
+                    return updated_history, empty_msg, chart_html
+                
+                # Wire up the chat interface
+                msg.submit(
+                    process_and_display,
+                    inputs=[msg, chatbot],
+                    outputs=[chatbot, msg, chart_display]
+                )
+                
+                submit_btn.click(
+                    process_and_display,
+                    inputs=[msg, chatbot],
+                    outputs=[chatbot, msg, chart_display]
+                )
+                
+                clear_btn.click(
+                    lambda: ([], ""),
+                    outputs=[chatbot, chart_display]
+                )
+        
+        gr.Markdown("---")
+        gr.Markdown(f"**Enabled Plugins:** Schema Detector, Text Processor, Table Formatter, Date Normalizer, CSV Handler, Report Generator, Excel Handler, Document Memory, Data Cleaner, Statistical Analyzer, Time Series Analyzer, Chart Generator, Conversation Memory")
+        gr.Markdown("*Powered by Universal AI Agent Development Platform*")
+    
+    return demo
+
+# ============================================================================
+# MAIN ENTRY POINT
+# ============================================================================
+
+if __name__ == "__main__":
+    # Check for environment variables
+    if not os.getenv("OPENAI_API_KEY"):
+        print("⚠️  Warning: OPENAI_API_KEY not set (not required for basic analytics)")
+    
+    # Launch application
+    print("🚀 Launching Universal AI Platform...")
+    demo = create_ui()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )

config.json

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+{
+    "agents": [
+        {
+            "id": "agent1",
+            "name": "DataExtractor",
+            "role": "Extracts and interprets data from uploaded Excel files",
+            "model": "gpt-3.5-turbo",
+            "temperature": 0.5,
+            "max_tokens": 500,
+            "system_prompt": "Identify and extract all relevant data from the uploaded Excel sheet.",
+            "capabilities": [
+                "data_extraction",
+                "data_interpretation"
+            ],
+            "status": "idle",
+            "tasks_completed": 0,
+            "tasks_failed": 0,
+            "tokens_used": 0,
+            "avg_response_time": 0.0,
+            "progress": 0.0,
+            "current_task": null
+        },
+        {
+            "id": "agent2",
+            "name": "DataAnalyzer",
+            "role": "Analyzes extracted data for patterns and insights",
+            "model": "gpt-4",
+            "temperature": 0.7,
+            "max_tokens": 750,
+            "system_prompt": "Analyze the extracted data to find patterns, insights, and possible correlations.",
+            "capabilities": [
+                "data_analysis",
+                "pattern_recognition"
+            ],
+            "status": "idle",
+            "tasks_completed": 0,
+            "tasks_failed": 0,
+            "tokens_used": 0,
+            "avg_response_time": 0.0,
+            "progress": 0.0,
+            "current_task": null
+        },
+        {
+            "id": "agent3",
+            "name": "ChatbotInterface",
+            "role": "Provides a conversational interface for user interaction",
+            "model": "gpt-3.5-turbo",
+            "temperature": 0.6,
+            "max_tokens": 600,
+            "system_prompt": "Interact with the user to provide data summaries, answer queries, and receive instructions for further data manipulation.",
+            "capabilities": [
+                "user_interaction",
+                "data_summarization"
+            ],
+            "status": "idle",
+            "tasks_completed": 0,
+            "tasks_failed": 0,
+            "tokens_used": 0,
+            "avg_response_time": 0.0,
+            "progress": 0.0,
+            "current_task": null
+        },
+        {
+            "id": "agent4",
+            "name": "DataModifier",
+            "role": "Modifies and updates data as per user or system requirements",
+            "model": "gpt-4",
+            "temperature": 0.4,
+            "max_tokens": 1000,
+            "system_prompt": "Modify the data according to user commands or predefined rules.",
+            "capabilities": [
+                "data_modification",
+                "data_updating"
+            ],
+            "status": "idle",
+            "tasks_completed": 0,
+            "tasks_failed": 0,
+            "tokens_used": 0,
+            "avg_response_time": 0.0,
+            "progress": 0.0,
+            "current_task": null
+        }
+    ],
+    "segments": [
+        {
+            "id": "segment1",
+            "name": "DataProcessing",
+            "objective": "Process data from extraction to analysis",
+            "agent_ids": [
+                "agent1",
+                "agent2"
+            ],
+            "workflow": "sequential",
+            "coordination_strategy": "priority",
+            "completion": 0.0,
+            "status": "pending",
+            "tokens_used": 0,
+            "cost": 0.0
+        },
+        {
+            "id": "segment2",
+            "name": "UserInteraction",
+            "objective": "Interact with the user to refine data processing and output",
+            "agent_ids": [
+                "agent3",
+                "agent4"
+            ],
+            "workflow": "parallel",
+            "coordination_strategy": "round_robin",
+            "completion": 0.0,
+            "status": "pending",
+            "tokens_used": 0,
+            "cost": 0.0
+        }
+    ],
+    "plugins": {
+        "schema_detector": true,
+        "text_processor": true,
+        "table_formatter": true,
+        "date_normalizer": true,
+        "csv_handler": true,
+        "report_generator": true,
+        "excel_handler": true,
+        "document_memory": true,
+        "data_cleaner": true,
+        "statistical_analyzer": true,
+        "time_series_analyzer": true,
+        "chart_generator": true,
+        "conversation_memory": true
+    },
+    "dependencies": [
+        "markdown",
+        "numpy",
+        "openpyxl",
+        "pandas",
+        "plotly",
+        "tabulate",
+        "xlrd"
+    ]
+}

requirements.txt

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+gradio>=4.0.0
+markdown
+numpy
+numpy>=1.24.0
+openpyxl
+pandas
+pandas>=2.0.0
+plotly
+tabulate
+xlrd